Welcome to stickland!

I’ve been working on a little side project for the last year or so. I thought this might be a good time to share this with you, particularly since I probably (with a very high probability) won’t be making any more posts for the rest of the year due a few little things called a dissertation and a wedding 🙂

The idea was to create a digital learning environment for working with data cards, in an attempt to make stronger connections between data cards, data structures and data displays, and to make effective use of tablets/devices (particularly in large lecture groups like my current teaching situation). This first digital environment is based on the C@S stick people data cards I created last year, but could involve any population/data etc, since everything is created dynamically. The idea to use stick people (figures) for the data cards was based on material Rob Gould presented at the NZAMT conference in 2015 regarding the Introduction to Data Science (IDS) course the Mobilize team created for high school students.

In stickland, the members of its population (the C@S stick people) ride by on skateboards. The numbers displayed on each stick person are their unique three digit ID number. The environment is set up so that the stick people arrive to this stretch of road in stick land in a random order and at random times. Students could check this out by watching the stick people skate on by and recording their ID numbers. They should see no pattern to the numbers and be convinced that they can not predict what ID number the next stick person will have (well, I guess if you watched for long enough you would be able to predict the last ID number……)

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To select stick people to find out more about them, students click on the stick person as they skate past. Some of the stick people are faster than others (more about that next year!) so it’s not always easy to catch them. This means that it will take different times for students to collect the same number of data cards. As the stick people are selected, a stack of data cards starts to be built on the top right hand side of data card screen below.

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At this point we’re in a similar position to where we would be if we had given students a set of data cards each, or if we had asked them to select a random sample of data cards from a population bag. One of the really awesome things about data cards is the physical nature of them – students can move them around, sort them, line them up, etc. So in this digital environment, students can drag the stick people data card around by tapping their heads and dragging their finger.

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I love getting students to sort the data cards by a categorical variable (e.g. Facebook user) and then by another categorical variable (e.g. Snapchat user) to build ideas of two-way tables and conditioning.

You can also get students to make graphs out of the data cards (see one of Pip Arnold’s excellent resources along these lines here on Census At School NZ). In this digital environment, students can make the cards bigger or smaller, and can move into “dot” mode as they move into graphical representations by encoding the data.

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To help students build understanding of what are essential features of their graphs, there is a drawing tool so they can add in additional information like axes, labels, numbers etc. I can see a whole lot of potential here, particularly with students exploring different ways to organise and display data.

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To help build understanding of the relationship between units, variables and data structures (specifically rectangular data sets), an interactive spreadsheet builds below the data card screen as the cards are collected. When a student selects a data card, this stick person’s row of data is highlighted in the spreadsheet, and vice versa. To check each student can match the data shown on the data card to the spreadsheet, data is missing from the spreadsheet (shown by grey boxes).

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Students will need to find the relevant stick person, read the card for the appropriate variable, and enter this data to make a complete data set. At the moment, I’ve set this feature so that there is missing data for 10 different stick people (one of each variable on the data card) and that the data can not be visualised using software (iNZight lite) until the missing data has been found.

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The final link is to explore the data using software like iNZight lite, which has been designed by Chris Wild to help students “get into data deeper and faster” (PS I’m not sure if that is an exact quote!). The data cards are not automatically linked to the data in iNZight lite, so if more data cards are collected, the iNZight button will need to be pressed again to update. I’m excited about getting students to explore relationships and build informal predictive models (after trying this out with the data cards earlier), and then checking these models out by easily selecting more stick people (see more about this kind of activity in my post about data challenges).